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SORTING AND SEARCHINGALGORITHMS: A COOKBOOK
BY THOMAS NIEMANN
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PREFACE
This is a collection of algorithms for sorting and searching. Descriptions are brief andintuitive, with just enough theory thrown in to make you nervous. I assume you know a
high-level language, such as C, and that you are familiar with programming concepts
including arrays and pointers.
The first section introduces basic data structures and notation. The next sectionpresents several sorting algorithms. This is followed by a section on dictionaries,
structures that allow efficient insert, search, and delete operations. The last sectiondescribes algorithms that sort data and implement dictionaries for very large files. Sourcecode for each algorithm, in ANSI C, is available at the site listed below.
Most algorithms have also been translated to Visual Basic. If you are programming in
Visual Basic, I recommend you readVisual Basic Collections and Hash Tables, for anexplanation of hashing and node representation.
Permission to reproduce portions of this document is given provided the web site
listed below is referenced, and no additional restrictions apply. Source code, when part of
a software project, may be used freely without reference to the author.
THOMAS NIEMANNPortland, Oregon
niemannt@yahoo.com
http://members.xoom.com/thomasn
http://members.xoom.com/thomasn/v_man.htmhttp://members.xoom.com/thomasn/v_man.htmmailto:niemannt@yahoo.comhttp://members.xoom.com/thomasnhttp://members.xoom.com/thomasnmailto:niemannt@yahoo.comhttp://members.xoom.com/thomasn/v_man.htm8/6/2019 Thomas Niemann - Sorting & Searching
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CONTENTS
1. INTRODUCTION 4
1.1 Arrays 41.2 Linked Lists 5
1.3 Timing Estimates 6
1.4 Summary 6
2. SORTING 72.1 Insertion Sort 7
2.2 Shell Sort 8
2.3 Quicksort 92.4 Comparison 12
3. DICTIONARIES 14
3.1 Hash Tables 14
3.2 Binary Search Trees 18
3.3 Red-Black Trees 21
3.4 Skip Lists 243.5 Comparison 25
4. VERY LARGE FILES 28
4.1 External Sorting 28
4.2 B-Trees 31
5. BIBLIOGRAPHY 34
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1. Introduction
We'll start by briefly discussing general concepts, including arrays, linked-lists, andbig-O notation.
1.1 ArraysFigure 1-1 shows an array, seven elements long, containing numeric values. To search the
array sequentially, we may use the algorithm in Figure 1-2. The maximum number of
comparisons is 7, and occurs when the key we are searching for is inA[6].
4
7
16
20
37
38
43
0
1
2
3
4
5
6 Ub
M
Lb
Figure 1-1: An Array
Figure 1-2: Sequential Search
If the data is sorted, a binary search may be done (Figure 1-3). VariablesLb and Ub
keep track of the lower boundand upper boundof the array, respectively. We begin byexamining the middle element of the array. If the key we are searching for is less than the
middle element, then it must reside in the top half of the array. Thus, we set Ub to
(M - 1). This restricts our next iteration through the loop to the top half of the array. Inthis way, each iteration halves the size of the array to be searched. For example, the first
iteration will leave 3 items to test. After the second iteration, there will be one item left to
test. Therefore it takes only three iterations to find any number.
int function SequentialSearch (ArrayA, intLb , int Ub , int Key);
begin
for i =Lb to UbdoifA [ i ] = Keythen
return i;
return1;
end;
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Figure 1-3: Binary Search
This is a powerful method. Given an array of 1023 elements, we can narrow the search to511 elements in one comparison. After another comparison, and we're looking at only
255 elements. In fact, we can search the entire array in only 10 comparisons.
In addition to searching, we may wish to insert or delete entries. Unfortunately, anarray is not a good arrangement for these operations. For example, to insert the number
18 in Figure 1-1, we would need to shift A[3]A[6] down by one slot. Then we could
copy number 18 into A[3]. A similar problem arises when deleting numbers. To improvethe efficiency of insert and delete operations, linked lists may be used.
1.2 Linked Lists
4 7 16 20 37 38
#
43
18
X
P
Figure 1-4: A Linked List
In Figure 1-4 we have the same values stored in a linked list. Assuming pointers X and P,
as shown in the figure, value 18 may be inserted as follows:
X->Next = P->Next;P->Next = X;
Insertion and deletion operations are very efficient using linked lists. You may be
wondering how pointer P was set in the first place. Well, we had to do a sequential search
int functionBinarySearch (ArrayA , intLb , int Ub , int Key );
begin
do foreverM = ( Lb + Ub )/2;
if ( Key A[M]) then
Lb = M + 1;
else
returnM;
if(Lb > Ub) then
return1;
end;
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to find the insertion point X. Although we improved our performance for insertion and
deletion, it was done at the expense of search time.
1.3 Timing Estimates
Several methods may be used to compare the performance of algorithms. One way issimply to run several tests for each algorithm and compare the timings. Another way is to
estimate the time required. For example, we may state that search time is O(n) (big-
oh ofn). This means that search time, for large n, is proportional to the number of items nin the list. Consequently, we would expect search time to triple if our list increased in size
by a factor of three. The big-O notation does not describe the exact time that an algorithm
takes, but only indicates an upper bound on execution time within a constant factor. If an
algorithm takes O(n2) time, then execution time grows no worse than the square of the
size of the list.
n lg n n lg n n1.25
n2
1 0 0 1 116 4 64 32 256
256 8 2,048 1,024 65,536
4,096 12 49,152 32,768 16,777,216
65,536 16 1,048,565 1,048,476 4,294,967,296
1,048,476 20 20,969,520 33,554,432 1,099,301,922,576
16,775,616 24 402,614,784 1,073,613,825 281,421,292,179,456
Table 1-1: Growth Rates
Table 1-1 illustrates growth rates for various functions. A growth rate of O(lg n)
occurs for algorithms similar to the binary search. The lg (logarithm, base 2) function
increases by one when n is doubled. Recall that we can search twice as many items withone more comparison in the binary search. Thus the binary search is a O(lg n) algorithm.
If the values in Table 1-1 represented microseconds, then a O(lg n) algorithm may
take 20 microseconds to process 1,048,476 items, a O(n1.25
) algorithm might take 33
seconds, and a O(n2) algorithm might take up to 12 days! In the following chapters a
timing estimate for each algorithm, using big-O notation, will be included. For a more
formal derivation of these formulas you may wish to consult the references.
1.4 Summary
As we have seen, sorted arrays may be searched efficiently using a binary search.
However, we must have a sorted array to start with. In the next section various ways tosort arrays will be examined. It turns out that this is computationally expensive, and
considerable research has been done to make sorting algorithms as efficient as possible.
Linked lists improved the efficiency of insert and delete operations, but searches weresequential and time-consuming. Algorithms exist that do all three operations efficiently,
and they will be the discussed in the section on dictionaries.
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2. Sorting
Several algorithms are presented, including insertion sort, shell sort, and quicksort.Sorting by insertion is the simplest method, and doesnt require any additional storage.
Shell sort is a simple modification that improves performance significantly. Probably the
most efficient and popular method is quicksort, and is the method of choice for largearrays.
2.1 Insertion Sort
One of the simplest methods to sort an array is an insertion sort. An example of an
insertion sort occurs in everyday life while playing cards. To sort the cards in your hand
you extract a card, shift the remaining cards, and then insert the extracted card in thecorrect place. This process is repeated until all the cards are in the correct sequence. Both
average and worst-case time is O(n2). For further reading, consult Knuth [1998].
TheoryStarting near the top of the array in Figure 2-1(a), we extract the 3. Then the above
elements are shifted down until we find the correct place to insert the 3. This process
repeats in Figure 2-1(b) with the next number. Finally, in Figure 2-1(c), we complete thesort by inserting 2 in the correct place.
4
1
2
4
3
1
2
4
1
2
3
4
1
2
3
4
2
3
4
1
2
3
4
2
1
3
4
2
1
3
4
1
3
4
2
1
3
4
1
2
3
4
(a )
(b)
(c)
Figure 2-1: Insertion Sort
Assuming there are n elements in the array, we must index through n 1 entries. For
each entry, we may need to examine and shift up to n 1 other entries, resulting in a
O(n2) algorithm. The insertion sort is an in-place sort. That is, we sort the array in-place.
No extra memory is required. The insertion sort is also a stable sort. Stable sorts retain
the original ordering of keys when identical keys are present in the input data.
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Implementation in C
Source for the insertion sort algorithm may be found in file ins.c. Typedef T and
comparison operator compGT should be altered to reflect the data stored in the table.
Implementation in Visual BasicSource for the insertion sort algorithm is included.
2.2 Shell Sort
Shell sort, developed by Donald L. Shell, is a non-stable in-place sort. Shell sortimproves on the efficiency of insertion sort by quickly shifting values to their destination.
Average sort time is O(n1.25), while worst-case time is O(n1.5). For further reading,
consult Knuth [1998].
TheoryIn Figure 2-2(a) we have an example of sorting by insertion. First we extract 1, shift 3and 5 down one slot, and then insert the 1, for a count of 2 shifts. In the next frame, twoshifts are required before we can insert the 2. The process continues until the last frame,
where a total of 2 + 2 + 1 = 5 shifts have been made.
In Figure 2-2(b) an example of shell sort is illustrated. We begin by doing an insertionsort using a spacing of two. In the first frame we examine numbers 3-1. Extracting 1, we
shift 3 down one slot for a shift count of 1. Next we examine numbers 5-2. We extract 2,
shift 5 down, and then insert 2. After sorting with a spacing of two, a final pass is madewith a spacing of one. This is simply the traditional insertion sort. The total shift count
using shell sort is 1+1+1 = 3. By using an initial spacing larger than one, we were able to
quickly shift values to their proper destination.
1
3
5
2
3
5
1
2
1
2
3
5
1
2
3
4
1
5
3
2
3
5
1
2
1
2
3
5
1
2
3
4
2s 2s 1s
1s 1s 1s
(a )
(b)
4 4 4 5
5444
Figure 2-2: Shell Sort
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Various spacings may be used to implement shell sort. Typically the array is sorted
with a large spacing, the spacing reduced, and the array sorted again. On the final sort,spacing is one. Although the shell sort is easy to comprehend, formal analysis is difficult.
In particular, optimal spacing values elude theoreticians. Knuth has experimented with
several values and recommends that spacing h for an array of size N be based on the
following formula:
Nhhhhh ttss +== ++ 211 whenwithstopand,13,1Let
Thus, values ofh are computed as follows:
1211)403(
401)133(
131)43(
41)13(
1
5
4
3
2
1
=+=
=+=
=+=
=+=
=
h
h
h
h
h
To sort 100 items we first find hs such that hs 100. For 100 items, h5 is selected. Ourfinal value (ht) is two steps lower, or h3. Therefore our sequence ofh values will be 13-4-
1. Once the initial h value has been determined, subsequent values may be calculated
using the formula
3/1 ss hh =
Implementation in C
Source for the shell sort algorithm may be found in file shl.c. Typedef T andcomparison operator compGT should be altered to reflect the data stored in the array. The
central portion of the algorithm is an insertion sort with a spacing of h.
Implementation in Visual Basic
Source for the shell sort algorithm is included.
2.3 Quicksort
Although the shell sort algorithm is significantly better than insertion sort, there is still
room for improvement. One of the most popular sorting algorithms is quicksort.Quicksort executes in O(n lg n) on average, and O(n
2) in the worst-case. However, with
proper precautions, worst-case behavior is very unlikely. Quicksort is a non-stable sort. Itis not an in-place sort as stack space is required. For further reading, consult
Cormen [1990].
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Theory
The quicksort algorithm works by partitioning the array to be sorted, then recursively
sorting each partition. In Partition (Figure 2-3), one of the array elements is selected as a
pivot value. Values smaller than the pivot value are placed to the left of the pivot, whilelarger values are placed to the right.
Figure 2-3: Quicksort Algorithm
In Figure 2-4(a), the pivot selected is 3. Indices are run starting at both ends of thearray. One index starts on the left and selects an element that is larger than the pivot,
while another index starts on the right and selects an element that is smaller than the
pivot. In this case, numbers 4 and 1 are selected. These elements are then exchanged, asis shown in Figure 2-4(b). This process repeats until all elements to the left of the pivot
are the pivot, and all items to the right of the pivot are the pivot. QuickSort
recursively sorts the two sub-arrays, resulting in the array shown in Figure 2-4(c).
int function Partition (ArrayA, intLb, int Ub);
begin
select a pivotfrom A[Lb]A[Ub];
reorderA[Lb]A[Ub] such that:
all values to the left of the pivotarepivot
all values to the right of the pivotarepivotreturnpivotposition;
end;
procedure QuickSort(ArrayA, intLb, int Ub);
begin
ifLb < Ub then
M = Partition (A, Lb, Ub);
QuickSort (A, Lb, M 1);
QuickSort (A, M + 1, Ub);
end;
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4 2 3 5 1
1 2 3 5 4
1 2 3 4 5
(a )
(b)
(c)
Lb Ub
pivot
Lb M Lb
Figure 2-4: Quicksort Example
As the process proceeds, it may be necessary to move the pivot so that correct
ordering is maintained. In this manner, QuickSortsucceeds in sorting the array. If we're
lucky the pivot selected will be the median of all values, equally dividing the array. For amoment, let's assume that this is the case. Since the array is split in half at each step, and
Partition must eventually examine all n elements, the run time is O(n lg n).
To find a pivot value, Partition could simply select the first element (A[Lb]). All
other values would be compared to the pivot value, and placed either to the left or right ofthe pivot as appropriate. However, there is one case that fails miserably. Suppose the
array was originally in order. Partition would always select the lowest value as a pivot
and split the array with one element in the left partition, and Ub Lb elements in theother. Each recursive call to quicksort would only diminish the size of the array to be
sorted by one. Therefore n recursive calls would be required to do the sort, resulting in a
O(n2) run time. One solution to this problem is to randomly select an item as a pivot. This
would make it extremely unlikely that worst-case behavior would occur.
Implementation in C
The source for the quicksort algorithm may be found in file qui.c. Typedef T and
comparison operator compGT should be altered to reflect the data stored in the array.
Several enhancements have been made to the basic quicksort algorithm:
The center element is selected as a pivot in partition. If the list is partiallyordered, this will be a good choice. Worst-case behavior occurs when the center
element happens to be the largest or smallest element each time partition is
invoked.
For short arrays, insertSort is called. Due to recursion and other overhead,quicksort is not an efficient algorithm to use on small arrays. Consequently, any
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array with fewer than 12 elements is sorted using an insertion sort. The optimal
cutoff value is not critical and varies based on the quality of generated code.
Tail recursion occurs when the last statement in a function is a call to the functionitself. Tail recursion may be replaced by iteration, resulting in a better utilization
of stack space. This has been done with the second call to QuickSort inFigure 2-3.
After an array is partitioned, the smallest partition is sorted first. This results in abetter utilization of stack space, as short partitions are quickly sorted and
dispensed with.
Included in file quilist.c is an implementation of quicksort for linked-lists. Also
included, in file qsort.c, is the source for qsort, an ANSI-C standard library function
usually implemented with quicksort. Recursive calls were replaced by explicit stackoperations. Table 2-1 shows timing statistics and stack utilization before and after the
enhancements were applied.
count before after before after
16 103 51 540 28
256 1,630 911 912 112
4,096 34,183 20,016 1,908 168
65,536 658,003 470,737 2,436 252
time (s) stacksize
Table 2-1: Effect of Enhancements on Speed and Stack Utilization
Implementation in Visual BasicSource for the quicksort sort algorithm is included.
2.4 Comparison
In this section we will compare the sorting algorithms covered: insertion sort, shell sort,
and quicksort. There are several factors that influence the choice of a sorting algorithm:
Stable sort. Recall that a stable sort will leave identical keys in the same relativeposition in the sorted output. Insertion sort is the only algorithm covered that isstable.
Space. An in-place sort does not require any extra space to accomplish its task.Both insertion sort and shell sort are in-place sorts. Quicksort requires stack space
for recursion, and therefore is not an in-place sort. Tinkering with the algorithm
considerably reduced the amount of time required.
Time. The time required to sort a dataset can easily become astronomical (Table1-1). Table 2-2 shows the relative timings for each method. The time required to
sort a randomly ordered dataset is shown in Table 2-3.
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Simplicity. The number of statements required for each algorithm may be found inTable 2-2. Simpler algorithms result in fewer programming errors.
method statements average time worst-case time
insertion sort 9 O (n2) O (n
2)
shell sort 17 O (n
1.25
) O (n
1.5
)quicksort 21 O (n lg n) O (n
2)
Table 2-2: Comparison of Methods
count insertion shell quicksort
16 39 s 45 s 51 s
256 4,969 s 1,230 s 911 s
4,096 1.315 sec .033 sec .020 sec
65,536 416.437 sec 1.254 sec .461 sec
Table 2-3: Sort Timings
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3. Dictionaries
Dictionaries are data structures that support search, insert, and delete operations. One ofthe most effective representations is a hashtable. Typically, a simple function is applied
to the key to determine its place in the dictionary. Also included are binary trees and red-
blacktrees. Both tree methods use a technique similar to the binary search algorithm tominimize the number of comparisons during search and update operations on the
dictionary. Finally, skip lists illustrate a simple approach that utilizes random numbers to
construct a dictionary.
3.1 Hash Tables
Hash tables are a simple and effective method to implement dictionaries. Average time tosearch for an element is O(1), while worst-case time is O(n).Cormen [1990] and Knuth
[1998]both contain excellent discussions on hashing.
TheoryA hash table is simply an array that is addressed via a hash function. For example, in
Figure 3-1,HashTable is an array with 8 elements. Each element is a pointer to a linked
list of numeric data. The hash function for this example simply divides the data key by 8,
and uses the remainder as an index into the table. This yields a number from 0 to 7. Since
the range of indices for HashTable is 0 to 7, we are guaranteed that the index is valid.
#
#
#
#
#
#
16
11
22
#
6
27
#
19
HashTable
0
1
2
3
4
5
6
7
Figure 3-1: A Hash Table
To insert a new item in the table, we hash the key to determine which list the item
goes on, and then insert the item at the beginning of the list. For example, to insert 11, wedivide 11 by 8 giving a remainder of 3. Thus, 11 goes on the list starting at
HashTable[3]. To find a number, we hash the number and chain down the correct list
to see if it is in the table. To delete a number, we find the number and remove the node
from the linked list.Entries in the hash table are dynamically allocated and entered on a linked list
associated with each hash table entry. This technique is known as chaining. An
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alternative method, where all entries are stored in the hash table itself, is known as direct
or open addressing and may be found in the references.If the hash function is uniform, or equally distributes the data keys among the hash
table indices, then hashing effectively subdivides the list to be searched. Worst-case
behavior occurs when all keys hash to the same index. Then we simply have a single
linked list that must be sequentially searched. Consequently, it is important to choose agood hash function. Several methods may be used to hash key values. To illustrate the
techniques, I will assume unsigned char is 8-bits, unsigned short int is 16-bits, and
unsignedlong intis 32-bits.
Division method (tablesize = prime). This technique was used in the precedingexample. A HashValue, from 0 to (HashTableSize - 1), is computed by
dividing the key value by the size of the hash table and taking the remainder. For
example:
typedef int HashIndexType;
HashIndexType Hash(int Key) {
return Key % HashTableSize;}
Selecting an appropriate HashTableSize is important to the success of this
method. For example, a HashTableSize divisible by two would yield even hash
values for even Keys, and odd hash values for odd Keys. This is an undesirable
property, as all keys would hash to the even values if they happened to be even. If
HashTableSize is a power of two, then the hash function simply selects a
subset of the Key bits as the table index. To obtain a more random scattering,
HashTableSize should be a prime number not too close to a power of two.
Multiplication method (tablesize = 2n
). The multiplication method may be usedfor a HashTableSize that is a power of 2. The Key is multiplied by a constant,
and then the necessary bits are extracted to index into the table. Knuth
recommends using the fractional part of the product of the key and the golden
ratio, or ( 2/15 . For example, assuming a word size of 8 bits, the golden ratiois multiplied by 28 to obtain 158. The product of the 8-bit key and 158 results in a
16-bit integer. For a table size of 25 the 5 most significant bits of the least
significant word are extracted for the hash value. The following definitions may
be used for the multiplication method:
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/* 8-bit index */typedef unsigned char HashIndexType;static const HashIndexType K = 158;
/* 16-bit index */typedef unsigned short int HashIndexType;
static const HashIndexType K = 40503;
/* 32-bit index */typedef unsigned long int HashIndexType;static const HashIndexType K = 2654435769;
/* w=bitwidth(HashIndexType), size of table=2**m */static const int S = w - m;HashIndexType HashValue = (HashIndexType)(K*Key) >> S;
For example, if HashTableSize is 1024 (210
), then a 16-bit index is sufficient
and S would be assigned a value of 16 10 = 6. Thus, we have:
typedef unsigned short int HashIndexType;
HashIndexType Hash(int Key) {static const HashIndexType K = 40503;static const int S = 6;return (HashIndexType)(K * Key) >> S;
}
Variable string addition method (tablesize = 256). To hash a variable-lengthstring, each character is added, modulo 256, to a total. A HashValue, range
0-255, is computed.
typedef unsigned char HashIndexType;
HashIndexType Hash(char *str) {HashIndexType h = 0;while (*str) h += *str++;return h;
}
Variable string exclusive-or method (tablesize = 256). This method is similar tothe addition method, but successfully distinguishes similar words and anagrams.
To obtain a hash value in the range 0-255, all bytes in the string are exclusive-or'dtogether. However, in the process of doing each exclusive-or, a random
component is introduced.
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typedef unsigned char HashIndexType;unsigned char Rand8[256];
HashIndexType Hash(char *str) {unsigned char h = 0;while (*str) h = Rand8[h ^ *str++];
return h;}
Rand8 is a table of 256 8-bit unique random numbers. The exact ordering is not
critical. The exclusive-or method has its basis in cryptography, and is quite
effective (Pearson [1990]).
Variable string exclusive-or method (tablesize 65536). If we hash the stringtwice, we may derive a hash value for an arbitrary table size up to 65536. Thesecond time the string is hashed, one is added to the first character. Then the two
8-bit hash values are concatenated together to form a 16-bit hash value.
typedef unsigned short int HashIndexType;unsigned char Rand8[256];
HashIndexType Hash(char *str) {HashIndexType h;unsigned char h1, h2;
if (*str == 0) return 0;h1 = *str; h2 = *str + 1;str++;while (*str) {
h1 = Rand8[h1 ^ *str];h2 = Rand8[h2 ^ *str];
str++;}
/* h is in range 0..65535 */h = ((HashIndexType)h1
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size time size time
1 869 128 9
2 432 256 6
4 214 512 4
8 106 1024 4
16 54 2048 3
32 28 4096 364 15 8192 3
Table 3-1: HashTableSize vs. Average Search Time (s), 4096 entries
Implementation in C
Source for the hash table algorithm may be found in file has.c. Typedefs recType,
keyType and comparison operator compEQ should be altered to reflect the data stored in
the table. The hashTableSize must be determined and the hashTable allocated. The
division method was used in the hash function. Function insert allocates a new nodeand inserts it in the table. Function delete deletes and frees a node from the table.
Function find searches the table for a particular value.
Implementation in Visual Basic
Source for both object and array implementations is included. In addition, collectionsmay be used, as key lookup in a Visual Basic Collection is done by hashing.
3.2 Binary Search Trees
In the Introduction, we used thebinary search algorithm to find data stored in an array.This method is very effective, as each iteration reduced the number of items to search by
one-half. However, since data was stored in an array, insertions and deletions were notefficient. Binary search trees store data in nodes that are linked in a tree-like fashion. For
randomly inserted data, search time is O(lg n). Worst-case behavior occurs when ordered
data is inserted. In this case the search time is O(n). See Cormen [1990] for a moredetailed description.
Theory
A binary search tree is a tree where each node has a left and right child. Either child, or
both children, may be missing. Figure 3-2 illustrates a binary search tree. Assuming k
represents the value of a given node, then a binary search tree also has the following
property: all children to the left of the node have values smaller than k, and all children to
the right of the node have values larger than k. The top of a tree is known as the root, and
the exposed nodes at the bottom are known as leaves. In Figure 3-2, the root is node 20and the leaves are nodes 4, 16, 37, and 43. The heightof a tree is the length of the longest
path from root to leaf. For this example the tree height is 2.
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20
7
164
38
4337
Figure 3-2: A Binary Search Tree
To search a tree for a given value, we start at the root and work down. For example,
to search for 16, we first note that 16 < 20 and we traverse to the left child. The second
comparison finds that 16 > 7, so we traverse to the right child. On the third comparison,
we succeed.
4
7
16
20
37
38
43
Figure 3-3: An Unbalanced Binary Search Tree
Each comparison results in reducing the number of items to inspect by one-half. Inthis respect, the algorithm is similar to a binary search on an array. However, this is trueonly if the tree is balanced. Figure 3-3 shows another tree containing the same values.
While it is a binary search tree, its behavior is more like that of a linked list, with search
time increasing proportional to the number of elements stored.
Insertion and Deletion
Let us examine insertions in a binary search tree to determine the conditions that cancause an unbalanced tree. To insert an 18 in the tree in Figure 3-2, we first search for that
number. This causes us to arrive at node 16 with nowhere to go. Since 18 > 16, we
simply add node 18 to the right child of node 16 (Figure 3-4).
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20
7
164
38
4337
18
Figure 3-4: Binary Tree After Adding Node 18
Now we can see how an unbalanced tree can occur. If the data is presented in anascending sequence, each node will be added to the right of the previous node. This will
create one long chain, or linked list. However, if data is presented for insertion in a
random order, then a more balanced tree is possible.Deletions are similar, but require that the binary search tree property be maintained.
For example, if node 20 in Figure 3-4 is removed, it must be replaced by node 37. This
results in the tree shown in Figure 3-5. The rationale for this choice is as follows. Thesuccessor for node 20 must be chosen such that all nodes to the right are larger. Therefore
we need to select the smallest valued node to the right of node 20. To make the selection,
chain once to the right (node 38), and then chain to the left until the last node is found(node 37). This is the successor for node 20.
37
7
164
38
43
18
Figure 3-5: Binary Tree After Deleting Node 20
Implementation in C
Source for the binary search tree algorithm may be found in file bin.c. Typedefs
recType, keyType and comparison operators compLT and compEQ should be altered toreflect the data stored in the tree. Each node consists of left, right, and parent
pointers designating each child and the parent. The tree is based at root, and is initially
NULL. Function insert allocates a new node and inserts it in the tree. Function delete
deletes and frees a node from the tree. Function find searches the tree for a particular
value.
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Implementation in Visual Basic
Source for both objectand array implementations is included.
3.3 Red-Black TreesBinary search trees work best when they are balanced or the path length from root to any
leaf is within some bounds. The red-black tree algorithm is a method for balancing trees.The name derives from the fact that each node is colored redor black, and the color of
the node is instrumental in determining the balance of the tree. During insert and delete
operations, nodes may be rotated to maintain tree balance. Both average and worst-case
search time is O(lg n). See Cormen [1990]for details.
Theory
A red-black tree is a balanced binary search tree with the following properties:
1. Every node is colored red or black.
2. Every leaf is a NIL node, and is colored black.
3. If a node is red, then both its children are black.
4. Every simple path from a node to a descendant leaf contains the same number ofblack nodes.
The number of black nodes on a path from root to leaf is known as the black heightof atree. These properties guarantee that any path from the root to a leaf is no more than
twice as long as any other path. To see why this is true, consider a tree with a blackheight of two. The shortest distance from root to leaf is two, where both nodes are black.
The longest distance from root to leaf is four, where the nodes are colored (root to leaf):red, black, red, black. It is not possible to insert more black nodes as this would violate
property 4, the black-height requirement. Since red nodes must have black children
(property 3), having two red nodes in a row is not allowed. The largest path we canconstruct consists of an alternation of red-black nodes, or twice the length of a path
containing only black nodes. All operations on the tree must maintain the properties listed
above. In particular, operations that insert or delete items from the tree must abide bythese rules.
Insertion
To insert a node, we search the tree for an insertion point, and add the node to the tree.
The new node replaces an existing NIL node at the bottom of the tree, and has two NIL
nodes as children. In the implementation, a NIL node is simply a pointer to a common
sentinel node that is colored black. After insertion, the new node is colored red. Then the
parent of the node is examined to determine if the red-black tree properties have been
violated. If necessary, we recolor the node and do rotations to balance the tree.
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By inserting a red node with two NIL children, we have preserved black-height
property (property 4). However, property 3 may be violated. This property states that
both children of a red node must be black. Although both children of the new node are
black (they're NIL), consider the case where the parent of the new node is red. Inserting a
red node under a red parent would violate this property. There are two cases to consider:
Red parent, red uncle: Figure 3-6 illustrates a red-red violation. Node X is thenewly inserted node, with both parent and uncle colored red. A simple recoloring
removes the red-red violation. After recoloring, the grandparent (node B) must bechecked for validity, as its parent may be red. Note that this has the effect of
propagating a red node up the tree. On completion, the root of the tree is marked
black. If it was originally red, then this has the effect of increasing the black-height of the tree.
Red parent, black uncle: Figure 3-7 illustrates a red-red violation, where the uncleis colored black. Here the nodes may be rotated, with the subtrees adjusted as
shown. At this point the algorithm may terminate as there are no red-red conflictsand the top of the subtree (node A) is colored black. Note that if node X was
originally a right child, a left rotation would be done first, making the node a leftchild.
Each adjustment made while inserting a node causes us to travel up the tree one step. Atmost one rotation (2 if the node is a right child) will be done, as the algorithm terminates
in this case. The technique for deletion is similar.
black
r ed r ed
r ed
B
A
X
C
paren t u n cle
r ed
bla ck bla ck
r ed
B
A
X
C
black
Figure 3-6: Insertion Red Parent, Red Uncle
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black
r ed bla ck
r ed
B
A
X
C
parent uncle
black
r ed r ed
A
X
C
black
B
Figure 3-7: Insertion Red Parent, Black Uncle
Implementation in C
Source for the red-black tree algorithm may be found in file rbt.c. Typedefs recType,
keyType and comparison operators compLT and compEQ should be altered to reflect the
data stored in the tree. Each node consists of left, right, and parent pointers
designating each child and the parent. The node color is stored in color, and is either
RED or BLACK. All leaf nodes of the tree are sentinel nodes, to simplify coding. The
tree is based at root, and initially is a sentinel node.
Function insert allocates a new node and inserts it in the tree. Subsequently, it calls
insertFixup to ensure that the red-black tree properties are maintained. Function
delete deletes a node from the tree. To maintain red-black tree properties,
deleteFixup is called. Function find searches the tree for a particular value.
Implementation in Visual Basic
Source for both objectand array implementations is included.
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3.4 Skip Lists
Skip lists are linked lists that allow you to skip to the correct node. The performance
bottleneck inherent in a sequential scan is avoided, while insertion and deletion remain
relatively efficient. Average search time is O(lg n). Worst-case search time is O(n), but isextremely unlikely. An excellent reference for skip lists is Pugh [1990].
Theory
The indexing scheme employed in skip lists is similar in nature to the method used to
lookup names in an address book. To lookup a name, you index to the tab representing
the first character of the desired entry. In Figure 3-8, for example, the top-most list
represents a simple linked list with no tabs. Adding tabs (middle figure) facilitates thesearch. In this case, level-1 pointers are traversed. Once the correct segment of the list is
found, level-0 pointers are traversed to find the specific entry.
# abe art ben bob cal cat dan don
# abe art ben bob cal cat dan don
# abe art ben bob cal cat dan don
0
0
1
0
1
2
Figure 3-8: Skip List Construction
The indexing scheme may be extended as shown in the bottom figure, where we now
have an index to the index. To locate an item, level-2 pointers are traversed until the
correct segment of the list is identified. Subsequently, level-1 and level-0 pointers aretraversed.
During insertion the number of pointers required for a new node must be determined.
This is easily resolved using a probabilistic technique. A random number generator is
used to toss a computer coin. When inserting a new node, the coin is tossed to determineif it should be level-1. If you win, the coin is tossed again to determine if the node should
be level-2. Another win, and the coin is tossed to determine if the node should be level-3.
This process repeats until you lose. If only one level (level-0) is implemented, the datastructure is a simple linked-list with O(n) search time. However, if sufficient levels are
implemented, the skip list may be viewed as a tree with the root at the highest level, and
search time is O(lg n).
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The skip list algorithm has a probabilistic component, and thus a probabilistic bounds
on the time required to execute. However, these bounds are quite tight in normalcircumstances. For example, to search a list containing 1000 items, the probability that
search time will be 5 times the average is about 1 in 1,000,000,000,000,000,000.
Implementation in CSource for the skip list algorithm may be found in file skl.c. Typedefs recType,
keyType and comparison operators compLT and compEQ should be altered to reflect the
data stored in the list. In addition, MAXLEVEL should be set based on the maximum size
of the dataset.
To initialize, initList is called. The list header is allocated and initialized. To
indicate an empty list, all levels are set to point to the header. Function insert allocates
a new node, searches for the correct insertion point, and inserts it in the list. While
searching, the update array maintains pointers to the upper-level nodes encountered.
This information is subsequently used to establish correct links for the newly inserted
node. The newLevel is determined using a random number generator, and the nodeallocated. The forward links are then established using information from the update
array. Function delete deletes and frees a node, and is implemented in a similar
manner. Function find searches the list for a particular value.
Implementation in Visual Basic
Not implemented.
3.5 Comparison
We have seen several ways to construct dictionaries: hash tables, unbalanced binarysearch trees, red-black trees, and skip lists. There are several factors that influence the
choice of an algorithm:
Sorted output. If sorted output is required, then hash tables are not a viablealternative. Entries are stored in the table based on their hashed value, with no
other ordering. For binary trees, the story is different. An in-order tree walk will
produce a sorted list. For example:
void WalkTree(Node *P) { if (P == NIL) return; WalkTree(P->Left);
/* examine P->Data here */
WalkTree(P->Right);}WalkTree(Root);
To examine skip list nodes in order, simply chain through the level-0 pointers. For
example:
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Node *P = List.Hdr->Forward[0];while (P != NIL) {
/* examine P->Data here */
P = P->Forward[0];
}
Space. The amount of memory required to store a value should be minimized.This is especially true if many small nodes are to be allocated.
For hash tables, only one forward pointer per node is required. In addition,the hash table itself must be allocated.
For red-black trees, each node has a left, right, and parent pointer. Inaddition, the color of each node must be recorded. Although this requires
only one bit, more space may be allocated to ensure that the size of thestructure is properly aligned. Therefore each node in a red-black tree
requires enough space for 3-4 pointers.
For skip lists, each node has a level-0 forward pointer. The probability ofhaving a level-1 pointer is . The probability of having a level-2 pointer is
. In general, the number of forward pointers per node is
.24
1
2
11n =+++=
Time. The algorithm should be efficient. This is especially true if a large dataset isexpected. Table 3-2 compares the search time for each algorithm. Note that worst-
case behavior for hash tables and skip lists is extremely unlikely. Actual timingtests are described below.
Simplicity. If the algorithm is short and easy to understand, fewer mistakes maybe made. This not only makes your life easy, but the maintenance programmer
entrusted with the task of making repairs will appreciate any efforts you make inthis area. The number of statements required for each algorithm is listed in Table
3-2.
method statements average time worst-case time
hash table 26 O (1) O (n)
unbalanced tree 41 O (lg n) O (n)
red-black tree 120 O (lg n) O (lg n)
skip list 55 O (lg n) O (n)
Table 3-2: Comparison of Dictionaries
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Average time for insert, search, and delete operations on a database of 65,536 (216
)
randomly input items may be found in Table 3-3. For this test the hash table size was10,009 and 16 index levels were allowed for the skip list. Although there is some
variation in the timings for the four methods, they are close enough so that other
considerations should come into play when selecting an algorithm.
method insert search delete
hash table 18 8 10
unbalanced tree 37 17 26
red-black tree 40 16 37
skip list 48 31 35
Table 3-3: Average Time (s), 65536 Items, Random Input
order count hash table unbalanced tree red-black tree skip list
16 4 3 2 5
random 256 3 4 4 9
input 4,096 3 7 6 12
65,536 8 17 16 31
16 3 4 2 4
ordered 256 3 47 4 7
input 4,096 3 1,033 6 11
65,536 7 55,019 9 15
Table 3-4: Average Search Time (us)
Table 3-4 shows the average search time for two sets of data: a random set, where allvalues are unique, and an ordered set, where values are in ascending order. Ordered input
creates a worst-case scenario for unbalanced tree algorithms, as the tree ends up being a
simple linked list. The times shown are for a single search operation. If we were to search
for all items in a database of 65,536 values, a red-black tree algorithm would take.6 seconds, while an unbalanced tree algorithm would take 1 hour.
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4. Very Large Files
The previous algorithms have assumed that all data reside in memory. However, theremay be times when the dataset is too large, and alternative methods are required. In this
section, we will examine techniques for sorting (external sorts) and implementing
dictionaries (B-trees) for very large files.
4.1 External Sorting
One method for sorting a file is to load the file into memory, sort the data in memory,then write the results. When the file cannot be loaded into memory due to resource
limitations, an external sort applicable. We will implement an external sort using
replacement selection to establish initial runs, followed by a polyphase merge sorttomerge the runs into one sorted file. I highly recommend you consult Knuth [1998], as
many details have been omitted.
TheoryFor clarity, Ill assume that data is on one or more reels of magnetic tape. Figure 4-1
illustrates a 3-way polyphase merge. Initially, in phase A, all data is on tapes T1 and T2.
Assume that the beginning of each tape is at the bottom of the frame. There are twosequential runs of data on T1: 4-8, and 6-7. Tape T2 has one run: 5-9. At phase B, weve
merged the first run from tapes T1 (4-8) and T2 (5-9) into a longer run on tape T3 (4-5-8-
9). Phase C is simply renames the tapes, so we may repeat the merge again. In phase Dwe repeat the merge, with the final output on tape T3.
phase T1 T2 T3
A 76
84
95
B
7
6
9
8
5
4
C 98
5
4
7
6D 9
8
7
65
4
Figure 4-1: Merge Sort
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Several interesting details have been omitted from the previous illustration. For
example, how were the initial runs created? And, did you notice that they mergedperfectly, with no extra runs on any tapes? Before I explain the method used for
constructing initial runs, let me digress for a bit.
In 1202, Leonardo Fibonacci presented the following exercise in his Liber Abbaci
(Book of the Abacus): How many pairs of rabbits can be produced from a single pair ina years time? We may assume that each pair produces a new pair of offspring every
month, each pair becomes fertile at the age of one month, and that rabbits never die. Afterone month, there will be 2 pairs of rabbits; after two months there will be 3; the following
month the original pair and the pair born during the first month will both usher in a new
pair, and there will be 5 in all; and so on. This series, where each number is the sum ofthe two preceding numbers, is known as the Fibonacci sequence:
0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ... .
Curiously, the Fibonacci series has found widespread application to everything from the
arrangement of flowers on plants to studying the efficiency of Euclids algorithm.Theres even a Fibonacci Quarterly journal. And, as you might suspect, the Fibonacciseries has something to do with establishing initial runs for external sorts.
Recall that we initially had one run on tape T2, and 2 runs on tape T1. Note that the
numbers {1,2} are two sequential numbers in the Fibonacci series. After our first merge,we had one run on T1 and one run on T2. Note that the numbers {1,1} are two sequential
numbers in the Fibonacci series, only one notch down. We could predict, in fact, that if
we had 13 runs on T2, and 21 runs on T1 {13,21}, we would be left with 8 runs on T1
and 13 runs on T3 {8,13} after one pass. Successive passes would result in run counts of{5,8}, {3,5}, {2,3}, {1,1}, and {0,1}, for a total of 7 passes. This arrangement is ideal,
and will result in the minimum number of passes. Should data actually be on tape, this is
a big savings, as tapes must be mounted and rewound for each pass. For more than 2tapes, higher-orderFibonacci numbers are used.
Initially, all the data is on one tape. The tape is read, and runs are distributed to other
tapes in the system. After the initial runs are created, they are merged as described above.One method we could use to create initial runs is to read a batch of records into memory,
sort the records, and write them out. This process would continue until we had exhausted
the input tape. An alternative algorithm, replacement selection, allows for longer runs. A
buffer is allocated in memory to act as a holding place for several records. Initially, thebuffer is filled. Then, the following steps are repeated until the input is exhausted:
Select the record with the smallest key that is the key of the last record written.
If all keys are smaller than the key of the last record written, then we havereached the end of a run. Select the record with the smallest key for the firstrecord of the next run.
Write the selected record.
Replace the selected record with a new record from input.
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Figure 4-2 illustrates replacement selection for a small file. The beginning of the file
is to the right of each frame. To keep things simple, Ive allocated a 2-record buffer.Typically, such a buffer would hold thousands of records. We load the buffer in step B,
and write the record with the smallest key (6) in step C. This is replaced with the next
record (key 8). We select the smallest key 6 in step D. This is key 7. After writing key
7, we replace it with key 4. This process repeats until step F, where our last key writtenwas 8, and all keys are less than 8. At this point, we terminate the run, and start another.
Step Input Buffer Output
A 5-3-4-8-6-7
B 5-3-4-8 6-7
C 5-3-4 8-7 6
D 5-3 8-4 7-6
E 5 3-4 8-7-6
F 5-4 3 | 8-7-6
G 5 4-3 | 8-7-6
H 5-4-3 | 8-7-6
Figure 4-2: Replacement Selection
This strategy simply utilizes an intermediate buffer to hold values until the appropriate
time for output. Using random numbers as input, the average length of a run is twice the
length of the buffer. However, if the data is somewhat ordered, runs can be extremelylong. Thus, this method is more effective than doing partial sorts.
When selecting the next output record, we need to find the smallest key the last key
written. One way to do this is to scan the entire list, searching for the appropriate key.
However, when the buffer holds thousands of records, execution time becomes
prohibitive. An alternative method is to use a binary tree structure, so that we onlycompare lg n items.
Implementation in C
Source for the external sort algorithm may be found in file ext.c. Function makeRuns
calls readRec to read the next record. Function readRec employs the replacement
selection algorithm (utilizing a binary tree) to fetch the next record, and makeRuns
distributes the records in a Fibonacci distribution. If the number of runs is not a perfect
Fibonacci number, dummy runs are simulated at the beginning of each file. Function
mergeSort is then called to do a polyphase merge sort on the runs.
Implementation in Visual Basic
Not implemented.
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4.2 B-Trees
Dictionaries for very large files typically reside on secondary storage, such as a disk. The
dictionary is implemented as an index to the actual file and contains the key and record
address of data. To implement a dictionary we could use red-black trees, replacingpointers with offsets from the beginning of the index file, and use random access to
reference nodes of the tree. However, every transition on a link would imply a disk
access, and would be prohibitively expensive. Recall that low-level disk I/O accesses disk
by sectors (typically 256 bytes). We could equate node size to sector size, and groupseveral keys together in each node to minimize the number of I/O operations. This is the
principle behind B-trees. Good references for B-trees includeKnuth [1998] and Cormen
[1998]. For B+-trees, consult Aho [1983].
Theory
Figure 4-3 illustrates a B-tree with 3 keys/node. Keys in internal nodes are surrounded bypointers, or record offsets, to keys that are less than or greater than, the key value. For
example, all keys less than 22 are to the left and all keys greater than 22 are to the right.For simplicity, I have not shown the record address associated with each key.
26
22
10 16
4 6 8 12 14 18 20 24 28 30
Figure 4-3: B-Tree
We can locate any key in this 2-level tree with three disk accesses. If we were togroup 100 keys/node, we could search over 1,000,000 keys in only three reads. To ensure
this property holds, we must maintain a balanced tree during insertion and deletion.
During insertion, we examine the child node to verify that it is able to hold an additionalnode. If not, then a new sibling node is added to the tree, and the childs keys are
redistributed to make room for the new node. When descending for insertion and the root
is full, then the root is spilled to new children, and the level of the tree increases. Asimilar action is taken on deletion, where child nodes may be absorbed by the root. This
technique for altering the height of the tree maintains a balanced tree.
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B-Tree B*-Tree B+-Tree B ++-Tree
data stored in any node any node leaf only leaf only
on insert, split 1 x 1 2 x 1/2 2 x 1 3 x 2/3 1 x 1 2 x 1/2 3 x 1 4 x 3/4
on delete, join 2 x 1/2 1 x 1 3 x 2/3 2 x 1 2 x 1/2 1 x 1 3 x 1/2 2 x 3/4
Table 4-1: B-Tree Implementations
Several variants of the B-tree are listed in Table 4-1. The standardB-tree stores keys
and data in both internal and leaf nodes. When descending the tree during insertion, a fullchild node is first redistributed to adjacent nodes. If the adjacent nodes are also full, then
a new node is created, and the keys in the child are moved to the newly created node.
During deletion, children that are full first attempt to obtain keys from adjacent nodes.If the adjacent nodes are also full, then two nodes are joined to form one full node.
B*-trees are similar, only the nodes are kept 2/3 full. This results in better utilization of
space in the tree, and slightly better performance.
22
10 16 26
22 2416 18 2010 12 144 6 8 26 28 30
Figure 4-4: B+-Tree
Figure 4-4 illustrates a B+-tree. All keys are stored at the leaf level, with their
associated data values. Duplicates of the keys appear in internal parent nodes to guide the
search. Pointers have a slightly different meaning than in conventional B-trees. The left
pointer designates all keys less than the value, while the right pointer designates all keys
greater than or equal to (GE) the value. For example, all keys less than 22 are on the left
pointer, and all keys greater than or equal to 22 are on the right. Notice that key 22 is
duplicated in the leaf, where the associated data may be found. During insertion and
deletion, care must be taken to properly update parent nodes. When modifying the firstkey in a leaf, the last GE pointer found while descending the tree will require
modification to reflect the new key value. Since all keys are in leaf nodes, we may link
them for sequential access.The last method, B++-trees, is something of my own invention. The organization is
similar to B+-trees, except for the split/join strategy. Assume each node can hold kkeys,
and the root node holds 3kkeys. Before we descend to a child node during insertion, wecheck to see if it is full. If it is, the keys in the child node and two nodes adjacent to the
child are all merged and redistributed. If the two adjacent nodes are also full, then another
node is added, resulting in four nodes, each full. Before we descend to a child node
during deletion, we check to see if it is full. If it is, the keys in the child node and two
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nodes adjacent to the child are all merged and redistributed. If the two adjacent nodes are
also full, then they are merged into two nodes, each full. Note that in each case, theresulting nodes are full. This is halfway between full and completely full, allowing
for an equal number of insertions or deletions in the future.
Recall that the root node holds 3k keys. If the root is full during insertion, we
distribute the keys to four new nodes, each full. This increases the height of the tree.During deletion, we inspect the child nodes. If there are only three child nodes, and they
are all full, they are gathered into the root, and the height of the tree decreases.Another way of expressing the operation is to say we are gathering three nodes, and
then scattering them. In the case of insertion, where we need an extra node, we scatter to
four nodes. For deletion, where a node must be deleted, we scatter to two nodes. Thesymmetry of the operation allows the gather/scatter routines to be shared by insertion and
deletion in the implementation.
Implementation in C
Source for the B++
-tree algorithm may be found in file btr.c. In the implementation-
dependent section, youll need to define bAdrType and eAdrType, the types associated
with B-tree file offsets and data file offsets, respectively. Youll also need to provide a
callback function that is used by the B++
-tree algorithm to compare keys. Functions are
provided to insert/delete keys, find keys, and access keys sequentially. Function main, at
the bottom of the file, provides a simple illustration for insertion.The code provided allows for multiple indices to the same data. This was
implemented by returning a handle when the index is opened. Subsequent accesses are
done using the supplied handle. Duplicate keys are allowed. Within one index, all keysmust be the same length. A binary search was implemented to search each node. A
flexible buffering scheme allows nodes to be retained in memory until the space is
needed. If you expect access to be somewhat ordered, increasing the bufCt will reducepaging.
Implementation in Visual Basic
Not implemented.
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5. Bibliography
Aho, Alfred V. and Jeffrey D. Ullman [1983].Data Structures and Algorithms. Addison-Wesley, Reading, Massachusetts.
Cormen, Thomas H., Charles E. Leiserson and Ronald L. Rivest [1990]. Introduction toAlgorithms. McGraw-Hill, New York.
Knuth, Donald. E. [1998].The Art of Computer Programming, Volume 3, Sorting and
Searching. Addison-Wesley, Reading, Massachusetts.
Pearson, Peter K [1990]. Fast Hashing of Variable-Length Text Strings.
Communications of the ACM, 33(6):677-680, June 1990.
Pugh, William [1990]. Skip lists: A Probabilistic Alternative To Balanced Trees.
Communications of the ACM, 33(6):668-676, June 1990.
Stephens, Rod [1998]. Ready-to-Run Visual Basic
Algorithms. John Wiley & Sons,
Inc., New York.
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